Comparer des méthodes

	Filtrage Séquentiel par Monte-Carlo pour Séries Temporelles	Monte Carlo séquentiel
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1993	1993 (particle filter); 2006 (SMC samplers)
Auteur d'origine	Gordon, Salmond & Smith	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Type	Sequential Bayesian filtering algorithm	Sequential Bayesian computation
Source fondatrice	Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F — Radar and Signal Processing, 140(2), 107–113.	Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107–113.
Alias	particle filter, time series SMC, sequential particle filtering, bootstrap particle filter	SMC, particle filter, sequential importance resampling, SMC sampler
Apparentées	5	6
Résumé	Time series sequential Monte Carlo (SMC), commonly called the particle filter, is a Bayesian simulation method that tracks the hidden state of a dynamical system as observations arrive one at a time. A cloud of weighted random samples — particles — is propagated forward through the system dynamics, reweighted by how well each particle explains the new observation, and periodically resampled to keep the representation concentrated on plausible states.	Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions.